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bmn_net.py
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bmn_net.py
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# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
#
#Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#Unless required by applicable law or agreed to in writing, software
#distributed under the License is distributed on an "AS IS" BASIS,
#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#See the License for the specific language governing permissions and
#limitations under the License.
import paddle.fluid as fluid
from paddle.fluid import ParamAttr
import numpy as np
import math
DATATYPE = 'float32'
class BMN_NET(object):
def __init__(self, mode, cfg):
self.tscale = cfg["tscale"]
self.dscale = cfg["dscale"]
self.prop_boundary_ratio = cfg["prop_boundary_ratio"]
self.num_sample = cfg["num_sample"]
self.num_sample_perbin = cfg["num_sample_perbin"]
self.is_training = (mode == 'train')
self.hidden_dim_1d = 256
self.hidden_dim_2d = 128
self.hidden_dim_3d = 512
self._get_interp1d_mask()
self._get_mask()
def conv1d(self,
input,
num_k=256,
input_size=256,
size_k=3,
padding=1,
groups=1,
act='relu',
name="conv1d"):
fan_in = input_size * size_k * 1
k = 1. / math.sqrt(fan_in)
param_attr = fluid.initializer.Uniform(low=-k, high=k)
bias_attr = fluid.initializer.Uniform(low=-k, high=k)
input = fluid.layers.unsqueeze(input=input, axes=[2])
conv = fluid.layers.conv2d(
input=input,
num_filters=num_k,
filter_size=(1, size_k),
stride=1,
padding=(0, padding),
groups=groups,
act=act,
name=name,
param_attr=param_attr,
bias_attr=bias_attr)
conv = fluid.layers.squeeze(input=conv, axes=[2])
return conv
def conv2d(self,
input,
num_k=256,
size_k=3,
padding=1,
act='relu',
name='conv2d'):
conv = fluid.layers.conv2d(
input=input,
num_filters=num_k,
filter_size=size_k,
stride=1,
padding=padding,
act=act,
name=name)
return conv
def conv3d(self, input, num_k=512, name="PEM_3d"):
conv = fluid.layers.conv3d(
input=input,
num_filters=num_k,
filter_size=(self.num_sample, 1, 1),
stride=(self.num_sample, 1, 1),
padding=0,
act='relu',
name=name)
return conv
def net(self, input):
# Base Module of BMN
x_1d = self.conv1d(
input,
input_size=400,
num_k=self.hidden_dim_1d,
size_k=3,
padding=1,
groups=4,
act="relu",
name="Base_1")
x_1d = self.conv1d(
x_1d,
num_k=self.hidden_dim_1d,
size_k=3,
padding=1,
groups=4,
act="relu",
name="Base_2")
# Temporal Evaluation Module of BMN
x_1d_s = self.conv1d(
x_1d,
num_k=self.hidden_dim_1d,
size_k=3,
padding=1,
groups=4,
act="relu",
name="TEM_s1")
x_1d_s = self.conv1d(
x_1d_s, num_k=1, size_k=1, padding=0, act="sigmoid", name="TEM_s2")
x_1d_e = self.conv1d(
x_1d,
num_k=self.hidden_dim_1d,
size_k=3,
padding=1,
groups=4,
act="relu",
name="TEM_e1")
x_1d_e = self.conv1d(
x_1d_e, num_k=1, size_k=1, padding=0, act="sigmoid", name="TEM_e2")
x_1d_s = fluid.layers.squeeze(input=x_1d_s, axes=[1])
x_1d_e = fluid.layers.squeeze(input=x_1d_e, axes=[1])
# Proposal Evaluation Module of BMN
x_1d = self.conv1d(
x_1d,
num_k=self.hidden_dim_2d,
size_k=3,
padding=1,
act="relu",
name="PEM_1d")
x_3d = self._boundary_matching_layer(x_1d)
x_3d = self.conv3d(x_3d, self.hidden_dim_3d, name="PEM_3d1")
x_2d = fluid.layers.squeeze(input=x_3d, axes=[2])
x_2d = self.conv2d(
x_2d,
self.hidden_dim_2d,
size_k=1,
padding=0,
act='relu',
name="PEM_2d1")
x_2d = self.conv2d(
x_2d,
self.hidden_dim_2d,
size_k=3,
padding=1,
act='relu',
name="PEM_2d2")
x_2d = self.conv2d(
x_2d,
self.hidden_dim_2d,
size_k=3,
padding=1,
act='relu',
name="PEM_2d3")
x_2d = self.conv2d(
x_2d, 2, size_k=1, padding=0, act='sigmoid', name="PEM_2d4")
return x_2d, x_1d_s, x_1d_e
def _get_mask(self):
bm_mask = []
for idx in range(self.dscale):
mask_vector = [1 for i in range(self.tscale - idx)
] + [0 for i in range(idx)]
bm_mask.append(mask_vector)
bm_mask = np.array(bm_mask, dtype=np.float32)
self.bm_mask = fluid.layers.create_global_var(
shape=[self.dscale, self.tscale],
value=0,
dtype=DATATYPE,
persistable=True)
fluid.layers.assign(bm_mask, self.bm_mask)
self.bm_mask.stop_gradient = True
def _boundary_matching_layer(self, x):
out = fluid.layers.matmul(x, self.sample_mask)
out = fluid.layers.reshape(
x=out, shape=[0, 0, -1, self.dscale, self.tscale])
return out
def _get_interp1d_bin_mask(self, seg_xmin, seg_xmax, tscale, num_sample,
num_sample_perbin):
# generate sample mask for a boundary-matching pair
plen = float(seg_xmax - seg_xmin)
plen_sample = plen / (num_sample * num_sample_perbin - 1.0)
total_samples = [
seg_xmin + plen_sample * ii
for ii in range(num_sample * num_sample_perbin)
]
p_mask = []
for idx in range(num_sample):
bin_samples = total_samples[idx * num_sample_perbin:(idx + 1) *
num_sample_perbin]
bin_vector = np.zeros([tscale])
for sample in bin_samples:
sample_upper = math.ceil(sample)
sample_decimal, sample_down = math.modf(sample)
if int(sample_down) <= (tscale - 1) and int(sample_down) >= 0:
bin_vector[int(sample_down)] += 1 - sample_decimal
if int(sample_upper) <= (tscale - 1) and int(sample_upper) >= 0:
bin_vector[int(sample_upper)] += sample_decimal
bin_vector = 1.0 / num_sample_perbin * bin_vector
p_mask.append(bin_vector)
p_mask = np.stack(p_mask, axis=1)
return p_mask
def _get_interp1d_mask(self):
# generate sample mask for each point in Boundary-Matching Map
mask_mat = []
for start_index in range(self.tscale):
mask_mat_vector = []
for duration_index in range(self.dscale):
if start_index + duration_index < self.tscale:
p_xmin = start_index
p_xmax = start_index + duration_index
center_len = float(p_xmax - p_xmin) + 1
sample_xmin = p_xmin - center_len * self.prop_boundary_ratio
sample_xmax = p_xmax + center_len * self.prop_boundary_ratio
p_mask = self._get_interp1d_bin_mask(
sample_xmin, sample_xmax, self.tscale, self.num_sample,
self.num_sample_perbin)
else:
p_mask = np.zeros([self.tscale, self.num_sample])
mask_mat_vector.append(p_mask)
mask_mat_vector = np.stack(mask_mat_vector, axis=2)
mask_mat.append(mask_mat_vector)
mask_mat = np.stack(mask_mat, axis=3)
mask_mat = mask_mat.astype(np.float32)
self.sample_mask = fluid.layers.create_parameter(
shape=[self.tscale, self.num_sample, self.dscale, self.tscale],
dtype=DATATYPE,
attr=fluid.ParamAttr(
name="sample_mask", trainable=False),
default_initializer=fluid.initializer.NumpyArrayInitializer(
mask_mat))
self.sample_mask = fluid.layers.reshape(
x=self.sample_mask, shape=[self.tscale, -1], inplace=True)
self.sample_mask.stop_gradient = True
def tem_loss_func(self, pred_start, pred_end, gt_start, gt_end):
def bi_loss(pred_score, gt_label):
pred_score = fluid.layers.reshape(
x=pred_score, shape=[-1], inplace=True)
gt_label = fluid.layers.reshape(
x=gt_label, shape=[-1], inplace=False)
gt_label.stop_gradient = True
pmask = fluid.layers.cast(x=(gt_label > 0.5), dtype=DATATYPE)
num_entries = fluid.layers.cast(
fluid.layers.shape(pmask), dtype=DATATYPE)
num_positive = fluid.layers.cast(
fluid.layers.reduce_sum(pmask), dtype=DATATYPE)
ratio = num_entries / num_positive
coef_0 = 0.5 * ratio / (ratio - 1)
coef_1 = 0.5 * ratio
epsilon = 0.000001
loss_pos = fluid.layers.elementwise_mul(
fluid.layers.log(pred_score + epsilon), pmask)
loss_pos = coef_1 * fluid.layers.reduce_mean(loss_pos)
loss_neg = fluid.layers.elementwise_mul(
fluid.layers.log(1.0 - pred_score + epsilon), (1.0 - pmask))
loss_neg = coef_0 * fluid.layers.reduce_mean(loss_neg)
loss = -1 * (loss_pos + loss_neg)
return loss
loss_start = bi_loss(pred_start, gt_start)
loss_end = bi_loss(pred_end, gt_end)
loss = loss_start + loss_end
return loss
def pem_reg_loss_func(self, pred_score, gt_iou_map, mask):
gt_iou_map = fluid.layers.elementwise_mul(gt_iou_map, mask)
u_hmask = fluid.layers.cast(x=gt_iou_map > 0.7, dtype=DATATYPE)
u_mmask = fluid.layers.logical_and(gt_iou_map <= 0.7, gt_iou_map > 0.3)
u_mmask = fluid.layers.cast(x=u_mmask, dtype=DATATYPE)
u_lmask = fluid.layers.logical_and(gt_iou_map <= 0.3, gt_iou_map >= 0.)
u_lmask = fluid.layers.cast(x=u_lmask, dtype=DATATYPE)
u_lmask = fluid.layers.elementwise_mul(u_lmask, mask)
num_h = fluid.layers.cast(
fluid.layers.reduce_sum(u_hmask), dtype=DATATYPE)
num_m = fluid.layers.cast(
fluid.layers.reduce_sum(u_mmask), dtype=DATATYPE)
num_l = fluid.layers.cast(
fluid.layers.reduce_sum(u_lmask), dtype=DATATYPE)
r_m = num_h / num_m
u_smmask = fluid.layers.uniform_random(
shape=[gt_iou_map.shape[1], gt_iou_map.shape[2]],
dtype=DATATYPE,
min=0.0,
max=1.0)
u_smmask = fluid.layers.elementwise_mul(u_mmask, u_smmask)
u_smmask = fluid.layers.cast(x=(u_smmask > (1. - r_m)), dtype=DATATYPE)
r_l = num_h / num_l
u_slmask = fluid.layers.uniform_random(
shape=[gt_iou_map.shape[1], gt_iou_map.shape[2]],
dtype=DATATYPE,
min=0.0,
max=1.0)
u_slmask = fluid.layers.elementwise_mul(u_lmask, u_slmask)
u_slmask = fluid.layers.cast(x=(u_slmask > (1. - r_l)), dtype=DATATYPE)
weights = u_hmask + u_smmask + u_slmask
weights.stop_gradient = True
loss = fluid.layers.square_error_cost(pred_score, gt_iou_map)
loss = fluid.layers.elementwise_mul(loss, weights)
loss = 0.5 * fluid.layers.reduce_sum(loss) / fluid.layers.reduce_sum(
weights)
return loss
def pem_cls_loss_func(self, pred_score, gt_iou_map, mask):
gt_iou_map = fluid.layers.elementwise_mul(gt_iou_map, mask)
gt_iou_map.stop_gradient = True
pmask = fluid.layers.cast(x=(gt_iou_map > 0.9), dtype=DATATYPE)
nmask = fluid.layers.cast(x=(gt_iou_map <= 0.9), dtype=DATATYPE)
nmask = fluid.layers.elementwise_mul(nmask, mask)
num_positive = fluid.layers.reduce_sum(pmask)
num_entries = num_positive + fluid.layers.reduce_sum(nmask)
ratio = num_entries / num_positive
coef_0 = 0.5 * ratio / (ratio - 1)
coef_1 = 0.5 * ratio
epsilon = 0.000001
loss_pos = fluid.layers.elementwise_mul(
fluid.layers.log(pred_score + epsilon), pmask)
loss_pos = coef_1 * fluid.layers.reduce_sum(loss_pos)
loss_neg = fluid.layers.elementwise_mul(
fluid.layers.log(1.0 - pred_score + epsilon), nmask)
loss_neg = coef_0 * fluid.layers.reduce_sum(loss_neg)
loss = -1 * (loss_pos + loss_neg) / num_entries
return loss
def bmn_loss_func(self, pred_bm, pred_start, pred_end, gt_iou_map, gt_start,
gt_end, bm_mask):
pred_bm_reg = fluid.layers.squeeze(
fluid.layers.slice(
pred_bm, axes=[1], starts=[0], ends=[1]),
axes=[1])
pred_bm_cls = fluid.layers.squeeze(
fluid.layers.slice(
pred_bm, axes=[1], starts=[1], ends=[2]),
axes=[1])
pem_reg_loss = self.pem_reg_loss_func(pred_bm_reg, gt_iou_map, bm_mask)
pem_cls_loss = self.pem_cls_loss_func(pred_bm_cls, gt_iou_map, bm_mask)
tem_loss = self.tem_loss_func(pred_start, pred_end, gt_start, gt_end)
loss = tem_loss + 10 * pem_reg_loss + pem_cls_loss
return loss, tem_loss, pem_reg_loss, pem_cls_loss